Assessing groundwater drought in Iran using GRACE data and machine learning - Ali Kashani; Hamid R. Safavi (Scientific Reports)
Abstract Groundwater serves as a critical freshwater reservoir globally, essential for ecosystem conservation and human well-being. Drought conditions adversely impact groundwater systems by first reducing recharge, followed by declines in groundwater levels and withdrawal potential, which can result in agricultural setbacks and irreversible consequences such as land subsidence. The introduction of the Gravity Recovery and Climate Experiment (GRACE) project marked a significant advancement in monitoring terrestrial water storage anomalies (TWSA), encompassing both surface and subsurface water. Traditional methods for assessing groundwater storage anomalies (GWSA), such as piezometric wells, have proven to be costly and inefficient, often lacking sufficient spatial and temporal coverage. Although GRACE data offers valuable insights, its large-scale nature presents challenges for localized basin and aquifer studies, compounded by data gaps resulting from a 15-month interruption during the transition to the GRACE-FO project. This study investigates the status of groundwater across six major river basins in Iran utilizing data from GRACE and its complementary Global Land Data Assimilation System (GLDAS) over a 255-month period from 2002 to 2023. The Extreme Gradient Boosting (XGBoost) algorithm is employed for downscaling TWSA to a resolution of 0.25°, achieving a high Pearson correlation (R) of 0.99 and a root mean square error (RMSE) of 22 mm. The downscaled GWSA, derived from the balance equation, exhibits an average correlation (R) of 0.93 and RMSE of 39 mm with observational data. Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. Results indicate an average groundwater depletion of 29 cm per year across Iran’s aquifers from 2002 to 2023, with the Caspian Sea basin experiencing the most significant decline. The GRACE Groundwater Drought Index (GGDI) is calculated and compared with the Standardized Precipitation Index (SPI), revealing an 8-month lag in drought propagation from meteorological to groundwater sources in Iran. Furthermore, correlations between the GGDI and teleconnection indices highlight their substantial influence on drought conditions in basins adjacent to major water sources. The results of this study, by emphasizing the reliability of satellite data and machine learning models in groundwater drought monitoring, can assist policymakers in enhancing groundwater resource management, strategic planning, and identifying critical basins, particularly in regions with limited observational data.